context-fundamentals

Agents miss constraints during long-context processing due to lost-in-the-middle attention failures. Effective context capacity is smaller than the window size, meaning low-signal noise buries high-value instructions unless you apply attention-favored placement and compaction.

Reduce token waste from verbose tool outputs by masking raw observations and retaining only recent high-value evidence. Compacting message history during long-running agent runs minimizes token cost while preserving critical reasoning context.

Progressive disclosure in context engineering loads only skill summaries at agent startup and fetches full details upon activation. This pattern reduces token budget consumption and improves reasoning quality by minimizing immediate context overload.

Debug agents ignoring system instructions by applying attention-favored placement of constraints and compacting message history. Treat context as an attention budget to ensure system instructions dominate inference-time reasoning over accumulated noise.

Increasing the context window does not automatically solve agent memory issues because effective context capacity is limited by attention mechanics. Without token budgeting and retrieval optimization, larger windows still suffer from lost-in-the-middle behavior.